Author
Yushan Zheng
Bio: Yushan Zheng is an academic researcher from Beihang University. The author has contributed to research in topics: Image retrieval & Digital pathology. The author has an hindex of 11, co-authored 36 publications receiving 421 citations.
Papers
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TL;DR: This work proposes skin lesion segmentation in dermoscopy images based on a convolutional neural network with an attention mechanism, which can preserve edge details and outperforms two typical segmentation networks and other state-of-the-art network methods.
108 citations
TL;DR: A novel nucleus-guided feature extraction framework based on convolutional neural network is proposed for histopathological images and achieves a better classification performance for breast lesions than the compared state-of-the-art methods.
108 citations
TL;DR: This paper proposes a novel aided-diagnosis framework of breast cancer using whole slide images, which shares the advantages of both HIC and CBHIR and is validated on fully annotated WSI data sets of breast tumors.
Abstract: Histopathological image classification (HIC) and content-based histopathological image retrieval (CBHIR) are two promising applications for the histopathological whole slide image (WSI) analysis. HIC can efficiently predict the type of lesion involved in a histopathological image. In general, HIC can aid pathologists in locating high-risk cancer regions from a WSI by providing a cancerous probability map for the WSI. In contrast, CBHIR was developed to allow searches for regions with similar content for a region of interest (ROI) from a database consisting of historical cases. Sets of cases with similar content are accessible to pathologists, which can provide more valuable references for diagnosis. A drawback of the recent CBHIR framework is that a query ROI needs to be manually selected from a WSI. An automatic CBHIR approach for a WSI-wise analysis needs to be developed. In this paper, we propose a novel aided-diagnosis framework of breast cancer using whole slide images, which shares the advantages of both HIC and CBHIR. In our framework, CBHIR is automatically processed throughout the WSI, based on which a probability map regarding the malignancy of breast tumors is calculated. Through the probability map, the malignant regions in WSIs can be easily recognized. Furthermore, the retrieval results corresponding to each sub-region of the WSIs are recorded during the automatic analysis and are available to pathologists during their diagnosis. Our method was validated on fully annotated WSI data sets of breast tumors. The experimental results certify the effectiveness of the proposed method.
85 citations
TL;DR: The proposed novel adaptive color deconvolution (ACD) algorithm can be efficiently solved and is effective to improve the performance of cancer image recognition, which is adequate for developing automatic CAD programs and systems based on WSIs.
56 citations
TL;DR: A novel cervical cell classification method based on Graph Convolutional Network (GCN) that can achieve the better classification performance and also can be potentially used in automatic screening system of cervical cytology.
52 citations
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28,685 citations
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3,940 citations
TL;DR: This paper strives to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.
299 citations
TL;DR: The proposed FGCNet model can assist radiologists to rapidly detect COVID-19 from chest CT images and gives better performance than all 15 state-of-the-art methods.
250 citations